AI Requirement Extraction for DUT Test Specification Documents
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Solution Overview
Problem
Test engineers face high overhead costs in time, training, and expertise due to the need to interact with multiple disparate software systems during test process development for a device under test (DUT), which extends time to market for products.
Innovation Solution
A generative AI-based system extracts test requirements from DUT documentation using large language models (LLM) on partitioned segments, supporting multi-modal inputs, and incorporating human-in-the-loop feedback for refinement and validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If test engineers interact with multiple disparate software systems to develop test processes, then test development completeness is improved, but overhead costs in time, training, and expertise increase
Solution Approach 1:
The patent combines multiple disparate software systems into a single integrated system that can perform test requirement extraction, test case generation, and test execution. The generative AI system consolidates functions that previously required separate tools, eliminating the need for test engineers to switch between multiple systems and reducing training requirements while maintaining comprehensive test development capabilities
Solution Approach 2:
The integrated test system performs multiple functions including requirement extraction from various documentation formats, test case generation, test execution, and result analysis. This multi-functional system replaces multiple specialized tools, allowing test engineers to accomplish comprehensive test development through a single platform that reduces overhead costs and accelerates time to market
2Reliability
If test engineers interact with multiple disparate software systems to develop test processes, then test development completeness is improved, but training and expertise requirements increase
Solution Approach 1:
The patent combines multiple disparate software systems into a single integrated system that can perform test requirement extraction, test case generation, and test execution. The generative AI system consolidates functions that previously required separate tools, eliminating the need for test engineers to switch between multiple systems and reducing training requirements while maintaining comprehensive test development capabilities
Solution Approach 2:
The system incorporates natural language processing capabilities that allow test engineers to interact with the system using everyday language rather than requiring specialized knowledge of multiple tool interfaces. The AI automatically understands requirements from documentation and generates appropriate test cases, reducing the expertise barrier and making the system easier to operate
3Measurement precision
If documentation is processed manually to extract test requirements, then accuracy is improved, but productivity decreases
Solution Approach 1:
The patent replaces manual mechanical processing of documentation with an automated generative AI system that uses natural language processing and machine learning. The system accurately extracts test requirements from various documentation formats including Word documents, PDFs, and spreadsheets, maintaining high accuracy while dramatically improving productivity by eliminating manual reading and analysis
Solution Approach 2:
The system transforms unstructured documentation text into structured test requirement data through parameter extraction. It identifies and extracts key parameters such as test conditions, expected results, and test steps from natural language documentation, converting them into structured formats that can be directly used for automated test case generation, thereby maintaining accuracy while enhancing productivity
Data Source
AI summary
Apparatuses, systems, and methods for extracting structured specification requirements from specification documents associated with a device under test (DUT) can include generating one or more semantic units based on DUT specification documentation and performing a structured large language model (LLM) call to generate test requirements for the DUT based on the generated one or more semantic units and an entity extraction task. The entity extraction task can be defined via a system prompt command that is responsive to presenting an end user with a system prompt for the entity extraction task. The one or more semantic units can be generated via portioning of DUT specification documentation.